A recent work [1]} describes a novel method to solve HMC problem, that preserves local and global information simultaneously to discover the local hierarchical relationship among classes.
Unlike this work, our architecture exploits recent neural network potentialities and facilitates the multi-class prediction for each deep layers to capture local context following the hierarchical structure of the information.
In our approach, we have a cascade of fully connected linear layers each one with softmax plus cross-entropy, where the output of a layer \(l-1\)  is the input of layer \(l\) ; instead, in [1]} the model has ReLu activation functions on two different layers fully connected with softmax and binary cross-entropy per block.
Another difference with [1]} is that the input of each layer \(l\)  fuse with the input, instead, in our approach the input per layer is the output of the previous layer.
The last difference is that our model uses local classification as final prediction in according to hierarchical multi-label classification task, instead of in HMCN-F the final layer is used as flat layer plus another layer that uses jointly local and global output information to obtain the final prediction.
